scholarly journals Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images

2020 ◽  
Vol 10 (22) ◽  
pp. 8170
Author(s):  
Pau Rodríguez ◽  
Diego Velazquez ◽  
Guillem Cucurull ◽  
Josep M. Gonfaus ◽  
F. Xavier Roca ◽  
...  
Keyword(s):  
Social Networks ◽  
Personality Traits ◽  
Personality Trait ◽  
Personality Assessment ◽  
Human Subjects ◽  
Ground Truth ◽  
Large Populations ◽  
Personality Questionnaires ◽  
Weakly Supervised ◽  
Highly Correlated

Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions.

Handwriting Analysis and Personality Assessment

2000 ◽  
Vol 5 (1) ◽  
pp. 44-51 ◽  
Author(s):  
Peter Greasley
Keyword(s):  
Personality Traits ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Empirical Studies ◽  
Handwriting Analysis ◽  
Recruitment Process ◽  
Academic Literature ◽  
Personnel Recruitment ◽  
The Subject ◽  
Better Than

It has been estimated that graphology is used by over 80% of European companies as part of their personnel recruitment process. And yet, after over three decades of research into the validity of graphology as a means of assessing personality, we are left with a legacy of equivocal results. For every experiment that has provided evidence to show that graphologists are able to identify personality traits from features of handwriting, there are just as many to show that, under rigorously controlled conditions, graphologists perform no better than chance expectations. In light of this confusion, this paper takes a different approach to the subject by focusing on the rationale and modus operandi of graphology. When we take a closer look at the academic literature, we note that there is no discussion of the actual rules by which graphologists make their assessments of personality from handwriting samples. Examination of these rules reveals a practice founded upon analogy, symbolism, and metaphor in the absence of empirical studies that have established the associations between particular features of handwriting and personality traits proposed by graphologists. These rules guide both popular graphology and that practiced by professional graphologists in personnel selection.

scholarly journals A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild

AI and Ethics ◽  
2021 ◽  
Author(s):  
Ryan Steed ◽  
Aylin Caliskan
Keyword(s):  
Social Psychology ◽  
Personality Traits ◽  
Face Processing ◽  
Human Subjects ◽  
Processing Technology ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
In The Wild ◽  
Interpretable Model ◽  
Shed Light

AbstractResearch in social psychology has shown that people’s biased, subjective judgments about another’s personality based solely on their appearance are not predictive of their actual personality traits. But researchers and companies often utilize computer vision models to predict similarly subjective personality attributes such as “employability”. We seek to determine whether state-of-the-art, black box face processing technology can learn human-like appearance biases. With features extracted with FaceNet, a widely used face recognition framework, we train a transfer learning model on human subjects’ first impressions of personality traits in other faces as measured by social psychologists. We find that features extracted with FaceNet can be used to predict human appearance bias scores for deliberately manipulated faces but not for randomly generated faces scored by humans. Additionally, in contrast to work with human biases in social psychology, the model does not find a significant signal correlating politicians’ vote shares with perceived competence bias. With Local Interpretable Model-Agnostic Explanations (LIME), we provide several explanations for this discrepancy. Our results suggest that some signals of appearance bias documented in social psychology are not embedded by the machine learning techniques we investigate. We shed light on the ways in which appearance bias could be embedded in face processing technology and cast further doubt on the practice of predicting subjective traits based on appearances.

scholarly journals The Association Between Personality and Homebound Status in Older Adults: Results From the NHATS

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 233-233
Author(s):  
Xiaocao Sun ◽  
Minhui Liu ◽  
Christina E Miyawaki ◽  
Yuxiao Li ◽  
Tianxue Hou ◽  
...  
Keyword(s):  
Older Adults ◽  
Personality Traits ◽  
Personality Assessment ◽  
Rating Scale ◽  
Community Dwelling ◽  
Cross Sectional ◽  
Health Related ◽  
Community Dwelling Older Adults ◽  
Ordinal Logistic Regression Model ◽  
Hispanic White

Abstract Personality is associated with predictors of homebound status like frailty, incident falls, and depression. It has been rarely investigated whether personality predicts homebound status among older adults. Using the combining cross-sectional data of the Year 2013 and Year 2014 data from the National Health and Aging Trends Study (NHATS), this study examined the association between personality traits and homebound status in a sample of community-dwelling older adults aged 65 years and older (N=2,788). Homebound status (non-homebound, semi-homebound, and homebound) was determined by the frequency, difficulty, and help of outdoor mobility. Personality traits, including conscientiousness, agreeableness, openness, extraversion, and neuroticism were assessed using the 10-item Midlife Development Inventory on a rating scale from 1 (not at all) to 4 (a lot). Each personality trait was included as a predictor in an ordinal logistic regression model to examine its association with homebound status after adjusting demographic and health-related covariates. The sample was on average 79±7.53 years old, non-Hispanic White (72.0%), female (58.6%), living alone (35.4%) or with spouse/partner only (37.4%). Seventy-four percent, 18%, and 8% of participants were non-homebound, semi-homebound, and homebound, respectively. Homebound participants tended to be less-educated older females. The average scores of conscientiousness, agreeableness, openness, extraversion, and neuroticism were 3.19±0.75, 3.57±0.56, 2.81±0.83, 3.13±0.75, and 2.22±0.86, respectively. Among these five personality traits, high conscientiousness (OR=1.34, p<0.001) and extraversion (OR=1.16, p=.03) were associated with a reduced likelihood of being homebound. These findings provided a basis for potential personality assessment to identify and protect individuals with high homebound risk.

scholarly journals An fMRI investigation of the relations between Extraversion, internalizing psychopathology, and neural activation following reward receipt in the Human Connectome Project sample

2020 ◽  
Vol 3 ◽  
Author(s):  
Courtland S. Hyatt ◽  
Emily S. Hallowell ◽  
Max M. Owens ◽  
Brandon M. Weiss ◽  
Lawrence H. Sweet ◽  
...  
Keyword(s):  
Personality Traits ◽  
Personality Trait ◽  
Factor Model ◽  
Five Factor Model ◽  
Effect Sizes ◽  
Levels Of Analysis ◽  
Future Research ◽  
Neural Activation ◽  
Internalizing Psychopathology ◽  
Human Connectome Project

Abstract Quantitative models of psychopathology (i.e., HiTOP) propose that personality and psychopathology are intertwined, such that the various processes that characterize personality traits may be useful in describing and predicting manifestations of psychopathology. In the current study, we used data from the Human Connectome Project (N = 1050) to investigate neural activation following receipt of a reward during an fMRI task as one shared mechanism that may be related to the personality trait Extraversion (specifically its sub-component Agentic Extraversion) and internalizing psychopathology. We also conducted exploratory analyses on the links between neural activation following reward receipt and the other Five-Factor Model personality traits, as well as separate analyses by gender. No significant relations (p < .005) were observed between any personality trait or index of psychopathology and neural activation following reward receipt, and most effect sizes were null to very small in nature (i.e., r < |.05|). We conclude by discussing the appropriate interpretation of these null findings, and provide suggestions for future research that spans psychological and neurobiological levels of analysis.

Effects of Teachers' Marital Status and Child's Family's Marital Status on Teachers' Ratings of a Child

1986 ◽  
Vol 58 (3) ◽  
pp. 959-964 ◽  
Author(s):  
Susan Goldstein-Hendley ◽  
Virginia Green ◽  
James R. Evans
Keyword(s):  
Marital Status ◽  
Personality Traits ◽  
Personality Trait ◽  
Rating Scale ◽  
Family Background ◽  
Family Status ◽  
Trait Rating

The purposes of this study were to assess whether knowledge of a child's family's marital status (divorced home/intact home/family status unknown) and/or teachers' marital status (single/divorced) would affect teachers' ratings of that child's personality traits and predicted behaviors. The study also sought to determine whether raters' marital status and knowledge of family background interacted with these teachers' ratings. The subjects were 27 married and 27 divorced teachers of preschool through Grade five. To test the hypotheses, two instruments were employed. The Personality Trait Rating Scale and the Predicted Behavior in School Scale were used by the teachers to rate behaviors of a 5-yr.-old child observed on a videotape. Knowledge of the child's family's marital status had no significant effect on teachers' ratings on either test. Teachers' own marital status had no significant effect on ratings, and no interaction was noted. Contrary to some earlier research, teachers were not biased in their ratings by knowledge of a child's family's marital status. Similarly, married teachers who had not experienced the divorce process themselves were no more positively or negatively biased in their ratings than were the divorced teachers.

scholarly journals Educational Attainment and Personality Are Genetically Intertwined

2017 ◽  
Vol 28 (11) ◽  
pp. 1631-1639 ◽  
Author(s):  
René Mõttus ◽  
Anu Realo ◽  
Uku Vainik ◽  
Jüri Allik ◽  
Tõnu Esko
Keyword(s):  
Educational Attainment ◽  
Personality Traits ◽  
Personality Trait ◽  
Factor Model ◽  
Five Factor Model ◽  
Molecular Genetic ◽  
Maximum Amount ◽  
Biological Processes ◽  
Psychological Traits ◽  
Polygenic Scores

Heritable variance in psychological traits may reflect genetic and biological processes that are not necessarily specific to these particular traits but pertain to a broader range of phenotypes. We tested the possibility that the personality domains of the five-factor model and their 30 facets, as rated by people themselves and their knowledgeable informants, reflect polygenic influences that have been previously associated with educational attainment. In a sample of more than 3,000 adult Estonians, education polygenic scores (EPSs), which are interpretable as estimates of molecular-genetic propensity for education, were correlated with various personality traits, particularly from the neuroticism and openness domains. The correlations of personality traits with phenotypic educational attainment closely mirrored their correlations with EPS. Moreover, EPS predicted an aggregate personality trait tailored to capture the maximum amount of variance in educational attainment almost as strongly as it predicted the attainment itself. We discuss possible interpretations and implications of these findings.

Personality traits, adjectives and gender

2020 ◽  
Vol 4 (1) ◽  
pp. 16-50
Author(s):  
Heiko Motschenbacher ◽  
Eka Roivainen
Keyword(s):  
Personality Traits ◽  
Personality Trait ◽  
Corpus Linguistics ◽  
American English ◽  
Psychological Analysis ◽  
Usage Patterns ◽  
Google Books ◽  
And Gender ◽  
Interdisciplinary Study ◽  
The Relationship

There have been linguistic studies on the gendering mechanisms of adjectives and psychological studies on the relationship between personality traits and gender, but the two fields have never entered into a dialogue on these issues. This article seeks to address this gap by presenting an interdisciplinary study that explores the gendering mechanisms associated with personality traits and personality trait-denoting adjectives. The findings of earlier work in this area and basic gendering mechanisms relevant to adjectives and personality traits are outlined. This is followed by a linguistic and a psychological analysis of the usage patterns of a set of personality trait adjectives. The linguistic section draws on corpus linguistics to explore the distribution of these adjectives with female, male and gender-neutral personal nouns in the Corpus of Contemporary American English. The psychological analysis relates the usage frequencies of personality trait adjectives with the nouns man, woman and person in the Google Books corpus to desirability ratings of the adjectives.

Personality and Ideology

2021 ◽  
Author(s):  
Danny Osborne ◽  
Nicole Satherley ◽  
Chris G. Sibley
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Personality Trait ◽  
Negative Relationship ◽  
Big Five Personality Traits ◽  
Openness To Experience ◽  
Big Five Personality ◽  
Vast Literature ◽  
Political Conservatism ◽  
The Relationship

Research since the 1990s reveals that openness to experience—a personality trait that captures interest in novelty, creativity, unconventionalism, and open-mindedness—correlates negatively with political conservatism. This chapter summarizes this vast literature by meta-analyzing 232 unique samples (N = 575,691) that examine the relationship between the Big Five personality traits and conservatism. The results reveal that the negative relationship between openness to experience and conservatism (r = −.145) is nearly twice as big as the next strongest correlation between personality and ideology (namely, conscientiousness and conservatism; r = .076). The associations between personality traits and conservatism were, however, substantively larger in Western, educated, industrialized, rich, and democratic (WEIRD) countries than in non-WEIRD countries. The chapter concludes by reviewing recent longitudinal work demonstrating that openness to experience and conservatism are non-causally related. Collectively, the chapter shows that openness to experience is by far the strongest (negative) correlate of conservatism but that there is little evidence that this association is causal.

Efficient Vector Partitioning Algorithms for Graph Clustering

2020 ◽  
Vol 1 (2) ◽  
pp. 101-123
Author(s):  
Hiroaki Shiokawa ◽  
Yasunori Futamura
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Ground Truth ◽  
Graph Clustering ◽  
Mining Communities ◽  
Fine Grained ◽  
Efficient Vector ◽  
Public Datasets ◽  
Many Core

This paper addressed the problem of finding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, \textit{CAV}, that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel efficient algorithm \textit{P-CAV} for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.

Mindful of Personality Trait Change: Are Treatment Effects on Personality Trait Change Ephemeral and Attributable to Changes in States?

2021 ◽  
Author(s):  
Mirjam Stieger ◽  
Mathias Allemand ◽  
Brent Roberts ◽  
Jordan Davis
Keyword(s):  
Substance Use ◽  
Personality Trait ◽  
Treatment Effects ◽  
Intervention Study ◽  
Measure Change ◽  
Conceptual Distinction ◽  
State Changes ◽  
Highly Correlated ◽  
Changes Over Time

Objective: Are treatment effects on personality trait change ephemeral and attributable to change in clinical states? Data of an intervention study was used to examine if change in clinical states (e.g., stress or depression) accounts for change in personality traits and to test whether both changes in traits and clinical states were independently associated with substance use. Method: Seventy-nine substance use patients (Mage = 25.3, SD = 2.7; 35 % female) took part at a 4-week intervention and completed a total of 15 bi-monthly assessments across 28 weeks to measure change in traits and states during and after treatment. Results: The results suggest that participants showed large trait and state changes over time, which happened rapidly with the majority occurring during the first four weeks. Trait and state changes were highly correlated, but not perfectly redundant. Significant variance in personality trait change remained after controlling for change in clinical states. Moreover, both trait and state change independently predicted substance use. Conclusion: Personality trait change occurred relatively fast and was maintained until the last follow-up assessment six months after the end of the intervention. Also, the findings point to the notion that the conceptual distinction between traits and states may not be as important as originally thought.

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